Local LLMs Are Already Daily-Driver Ready: M4 + GGUF, No Cloud Needed
I haven't paid Anthropic in a month. Not out of cheapness — I found local Qwen3-Coder indistinguishable from Claude on 80% of my daily work. There's still a gap. The gap just isn't in the last 20cm of daily productivity.
The Hardware Wall Is Behind Us
Saying "local models are slow" was accurate in 2024. Here's what hardware looks like today:
| Hardware | Price | Model it runs | Tokens/sec | Fits |
|---|---|---|---|---|
| MacBook Air M4 16GB | ¥9k | 7B Q4 | 45 t/s | Entry, light use |
| MacBook Pro M4 Pro 24GB | ¥18k | 14B Q5 | 35 t/s | Daily production |
| MacBook Pro M4 Max 48GB | ¥30k | 32B Q5 | 22 t/s | Heavy use, long context |
| Mac Studio M4 Ultra 128GB | ¥55k | 70B Q4 / 200B Q3 | 12 t/s | Near-frontier, private deploy |
M4 Pro 24GB is the sweet spot — an ¥18,000 laptop runs Qwen3-Coder 14B at Q5. Measured on mine: 1-2 seconds for a completion, 30-60 seconds for a review pass, 5-10 minutes for a whole-project refactor.
On the combined "latency plus accuracy" metric, that puts it in the same tier as cloud Claude.
Head-to-Head on My Own Projects
Bucketing my last two weeks of AI Coding usage:
| Task type | With Claude | With local Qwen3-Coder | Delta |
|---|---|---|---|
| Completion (line / block) | 1.2s | 0.4s | local 3x faster (no network) |
| Bug fix in a single function | 8s | 11s | cloud 30% faster |
| Write a small new feature | 25s | 38s | cloud 50% faster |
| Explain unfamiliar code | 12s | 15s | about even |
| Large refactor | 5-8 min | 6-10 min | about even |
| Complex spec → implementation | 90s | 240s | cloud 2.5x faster |
| Math / algorithm proofs | 60s | 200s | cloud 3x faster |
Conclusion: local wins the high-frequency small stuff (latency), cloud wins the low-frequency hard stuff (reasoning depth).
Weight that by volume and 80% of daily time sits in high-frequency small tasks. Which is why day to day, local just feels smoother.
What Local Gives You That Has Nothing to Do With Speed
Performance isn't the only variable. A few things cloud can never give you:
1. Actually offline Planes, subways, café wifi that won't hold, flaky international routes. These are 20% of my working hours in a month. Cloud models are worth exactly nothing in them.
2. Actually private On a recent client project, the code held the client's API keys, their schema, their business logic. Contractually none of it may leave my machine. Doesn't matter how good the cloud model is.
3. Zero marginal cost Running local costs about a buck of electricity overnight, and I don't wince at usage. Which changed my habits — I let the local model chew through things I'd never spend cloud tokens on: review the entire project, take the whole git log and analyze it. Those low-ROI high-frequency jobs turn out to be exactly where local shines.
4. No rate-limit dread No more "the 5-hour window is nearly gone." Vibemeter, the tool I built to watch rate limits, became half as important to me the moment I went local.
Who Should Stay on Cloud
I'm not claiming local replaces cloud. Three categories where I'd firmly stay on cloud:
1. Anything needing frontier reasoning New frameworks (a fresh Rust release, a new Bun API), hard math and algorithms, designing a new model protocol. A local 14B can't hold it. You need GPT-5.5 / Sonnet 4.6 class.
2. Multimodal Find the bug from a screenshot, turn a Figma into React. Local vision models are still catching up.
3. High-volume concurrency Need 20 agents running in parallel? One Mac starts choking at 3. Buy cloud API for that.
A Contrarian Prediction
By mid-2027, "local first, cloud as fallback" will be the default setup for senior engineers. Not to save money — cloud will keep getting cheaper — but to:
- Not be locked to any single model vendor
- Not be at the mercy of network, censorship, or regional differences
- Not let client data leave your machine
- Get that anywhere, anytime, don't-think-about-it feel
Cloud vendors are going to fight for your high-frequency small tasks — it's the segment they least want to lose, the biggest token consumer, the steadiest willingness to pay. Local models started taking that market in 2026, and there's no way cloud doesn't respond.
If You Want In
You don't need to wait for models to get better. Today is good enough.
Minimum setup:
- M4 Pro 24GB (or an equivalent Linux workstation — a 3090 24GB is fine)
- Ollama / LM Studio as the runner
- Qwen3-Coder 14B Q5 or DeepSeek-Coder-V3 16B as the default model
- Cline / Continue / Aider as the client
- Keep your Claude / GPT subscription as the fallback
A week in, you'll notice 80% of your work never touched the cloud. Then you'll start rethinking why you're still paying that $20-$200 a month.